The insurance industry is making use of various artificial intelligence applications to solve business problems, but perhaps the most versatile is predictive analytics. The ability to aggregate data from disparate sources for business intelligence allows business leaders in insurance to inform important decisions across departments.
In this article, we’ll take a look at some of the use-cases for predictive analytics software in the insurance industry.
Here at Emerj, we like to discuss use-cases with real-world examples, paying particular attention to case studies purporting success with AI software available to enterprises.
As such, in this report, we’ll be running through four vendors offering predictive analytics to insurance enterprises. These vendors offer software with value propositions such as:
- Rapidminer offers data science teams at insurance enterprises a platform for creating machine learning models for use cases such as customer churn prevention and fraud detection
- Alteryx and Guidewire offer software for ensuring that customer payouts aren’t overpaid
- Cloudera offers software that can prevent insurance employees from giving customers inaccurate quotes and detect fraud
We’ll start our analysis of the use cases for predictive analytics in insurance with RapidMiner’s platform for building machine learning models.
Customer Churn Prevention
RapidMiner offers a namesake software that it claims helps data science teams of insurance companies create and deploy predictive models for fraud and churn prevention.
The company advertises their software as a predictive analytics solution for insurance companies looking to gauge customer lifetime value. The software seems to use historical transaction data from customers to mark them with a high lifetime value and is able to reveal marketing options for that type of customer.
The company also claims the software can identify fraudulent insurance claims based on claims data exhibiting fraud in various forms. It should be noted that this is distinct from an AI-powered solution for anomaly detection. Predictive analytics for fraud prevention would be simply used to detect discrepancies identified from training on claims data. This type of software would use those discrepancies to alert the user that the detected behavior could be a precursor to fraud. Whereas anomaly detection would be able to detect and flag activities as fraud in real time while a user is interacting with or submitting a claim to an online or otherwise digital platform.
RapidMiner states the software’s machine learning model needs to be trained on tens of thousands of customer accounts and digitally documented insurance claims. The customer accounts used would ideally reveal trends or behaviors that point towards churn. The claims data would consist of both fraudulent and nonfraudulent claims, with the fraudulent claims being labeled as such. Then, a data scientist would expose the machine learning model to this data, which would train it to discern which data points correlate to customers with a high risk of churn and fraudulent claims.
The software could then predict which customers are most likely to end their relationship with the client insurer. It would also be able to predict if an insurance claim is fraudulent and prevent it from processing.